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Sequential Monitoring Of Changes In Dynamic Linear Models, Applied To The U.S. Housing Market

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  • Horváth, Lajos
  • Liu, Zhenya
  • Lu, Shanglin

Abstract

We propose a sequential monitoring scheme to find structural breaks in dynamic linear models. The monitoring scheme is based on a detector and a suitably chosen boundary function. If the detector crosses the boundary function, a structural break is detected. We provide the asymptotics for the procedure under the null hypothesis of stability. The consistency of the procedure is also proved. We derive the asymptotic distribution of the stopping time under the change point alternative. Monte Carlo simulation is used to show the size and the power of our method under several conditions. As an example, we study the real estate markets in Boston and Los Angeles, and at the national U.S. level. We find structural breaks in the markets, and we segment the data into stationary segments. It is observed that the autoregressive parameter is increasing but stays below 1.

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  • Horváth, Lajos & Liu, Zhenya & Lu, Shanglin, 2022. "Sequential Monitoring Of Changes In Dynamic Linear Models, Applied To The U.S. Housing Market," Econometric Theory, Cambridge University Press, vol. 38(2), pages 209-272, April.
    • Handle: RePEc:cup:etheor:v:38:y:2022:i:2:p:209-272_1
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    Cited by:

    1. Boubaker, Sabri & Liu, Zhenya & Sui, Tianqing & Zhai, Ling, 2022. "The mirror of history: How to statistically identify stock market bubble bursts," Journal of Economic Behavior & Organization, Elsevier, vol. 204(C), pages 128-147.
    2. Lazar, Emese & Wang, Shixuan & Xue, Xiaohan, 2023. "Loss function-based change point detection in risk measures," European Journal of Operational Research, Elsevier, vol. 310(1), pages 415-431.
    3. Lajos Horvath & Lorenzo Trapani & Shixuan Wang, 2024. "Sequential monitoring for explosive volatility regimes," Papers 2404.17885, arXiv.org.
    4. Lajos Horv'ath & Lorenzo Trapani, 2023. "Real-time monitoring with RCA models," Papers 2312.11710, arXiv.org.

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